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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Damasevicius, Robertas Taniar, David Rajinikanth, Venkatesan Lawal, Isah A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Monash University Faculty of Information Technology ClaytonVIC3800 Australia St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

Breast Cancer (BC) is one of the harsh conditions, which largely affects the women group. Due to its significance, a range of procedures are available for premature detection and early treatment to save the patient. The clinical level diagnosis of BC will be done using;(i) Image supported detection and (ii) Core-Needle-Biopsy (CNB) assisted confirmation. The proposed work aim to develop a computerized scheme to detect the Breast-Tumor-Section (BTS) from the beast MRI slices. This work implements a joint thresholding and segmentation methodology to enhance and extract the BTS from the 2D MRI slices. A tri-level thresholding based on Slime-Mould-Algorithm and Shannon's-Entropy (SMA+SE) is implemented to enhance the BTS and Watershed-Segmentation (WS) is implemented to mine the BTS. After extracting the BTS, a study between the BTS and Ground-Truth image is performed and the necessary Image-Performance-Values (IPV) are computed. In this work the axial, coronal and sagittal slices of 2D breast MRI are separately examined and the attained results are presented. © 2021 IEEE.

关键词： Tumors

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A Real-Time Density-Based Traffic Signal Control System 5

A Real-Time Density-Based Traffic Signal Control System

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5th International Conference on Information technology Research, ICITR 2020

作者： Chandrasekara, W.A.C.J.K. Rathnayaka, R.M.K.T. Chathuranga, L.L.G. Sabaragamuwa University Faculty of Applied Sciences Department of Computing and Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Applied Sciences Department of Physical Science and Technology Belihuloya Sri Lanka University of Ruhuna Faculty of Science Matara Sri Lanka

ISBN: (纸本)9781665414753

Traffic congestion and accidents have become two major issues in Sri Lanka today. These issues cause to create many social, economic and environmental problems. Lack of effective Traffic Light Control System is one of the reasons for it happen. This research proposed an approach to develop an effective real-time density-based traffic light control system. This research consists of two major parts;Image processing model for capture real-time data and ANN model for predict the results considering real-time data. Identify the best features from gathered data and minimize dimensionality between the features, by principal component analysis (PCA) to train a Neural Network model. Using cameras, lanes are monitored and capture image of its. Detection and counting of number of vehicles in each lane and length of queue is done by using image processing. The data from each lane is sent to the ANN unit. According to the count of vehicles, the trained model will be decided the lane and time limit that will need to allow green phase. The NN model has achieved 0.9274 accuracy in the training phase. Thus, the traffic lights at the intersections will have changed isolated and dynamically according to the conditions of real-time traffic when using this traffic light control system than the existing fixed time traffic light control system or traditional computation algorithms. This system reduces the average waiting time and increases the efficiency of traffic clearance. New adaptive traffic management also reduces the pollution due CO2 emission and also social and economic problems. © 2020 IEEE.

关键词： Traffic congestion

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An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility 2

An Empirical Investigation on Employing Machine Learning for...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Khatri, Anshu Mathi, Senthilkumar Ashiq Sheriff, A. Deepthika, R. Naveen Raaghavendran, G. Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Automated Detection of Schizophrenia from Brain MRI Slices using Optimized Deep-Features 7

Automated Detection of Schizophrenia from Brain MRI Slices u...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Taniar, David Damasevicius, Robertas Rajinikanth, Venkatesan Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Monash University Faculty of Information Technology ClaytonVIC3800 Australia Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

In humans, the incident rate of mental illness is gradually rising due to various causes. Schizophrenia is one of the chronic mental illness and its happing rate also rising in the current era. The patient with Schizophrenia will experience a confused mental condition and a timely recognition and treatment is essential to reduce the risk. The proposed work aims to implement a methodology to support the automated detection of Schizophrenia from the brain MRI slices of T1 modality (T1W). The assessment of brain MRI is executed using the pre-trained VGG16 system and the deep-features extracted are optimized with the Slime-Mould-Algorithm (SMA) and the reduced features are then considered to train, test and validate the binary classifiers employed in this work. This research is implemented using 500 images of each case (healthy/abnormal) and the attained result with the SVM-Cubic is superior compared to other classifiers considered in the automated disease detection system. © 2021 IEEE.

关键词： Magnetic resonance imaging

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U-Net Supported Segmentation of Ischemic-Stroke-Lesion from Brain MRI Slices 7

U-Net Supported Segmentation of Ischemic-Stroke-Lesion from ...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Damasevicius, Robertas Taniar, David Rajinikanth, Venkatesan Lawal, Isah A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Monash University Clayton Faculty of Information Technology VIC3800 Australia St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

The brain abnormality is one of the major sicknesses in human's health and the untreated brain defect will cause major illness. Ischemic stroke is one of the major medical emergencies and the timely diagnosis and treatment will save the patient from serious sickness. The proposed research employs the U-Net scheme to extort the Ischemic-Stoke-Lesion (ISL) from the brain MRI slices of ISLES2015 database. In this work, a pre-trained U-Net encoder-decoder system is employed to extort the ISL fragment from the chosen test image. After the extraction, a relative assessment is performed with the ground-truth available along with consequent test image. In this work, 20 patients' images (20 patient x 25 slices = 500 images) are adopted for the assessment and the general result achieved with the executed methodology helped to achieve a better value of Jaccard (>90%), Dice (>95%) and Accuracy (>98%) on the considered image dataset. © 2021 IEEE.

关键词： Diagnosis

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A Self-diagnosis Medical Chatbot for H5N1 Virus 11th

A Self-diagnosis Medical Chatbot for H5N1 Virus

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11th International Workshop on Learning technology for Education Challenges, LTEC 2023

作者： Kasinathan, Vinothini Mustapha, Aida Wei, Ooi Zhen Ching, Tham Hoong Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation Technology Part Malaysia Bukit Jalil Kuala Lumpur57000 Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Panchor86400 Malaysia

ISBN: (纸本)9783031347535

Chatbots are everywhere these days. This paper presents a chatbot, Hans, that is designed for dealing with the public during a H5N1 pandemic. Hans provides a natural way of educating the public on health safety measures with its conversational ability. Users are able to perform self-assessment for H5N1 infection and receive suggestions on preventive actions from the virus spread. Hans is developed based on the Verbot tool with C# programming platform. User Acceptance Test is carried out via an online questionnaire distributed among 30 respondents. The results revealed favorable feedback from the users on the roles and capacity of Hans in providing information and diagnosis on H5N1. Hans is hoped to assist the frontliners in terms of improving service times, reducing workload, and saving costs in manning the medical team. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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A New Approach to Road Incident Detection Leveraging Live Traffic Data: An Empirical Investigation 2

A New Approach to Road Incident Detection Leveraging Live Tr...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kumar Gannina, Aswin Ram Jaffarullah, Aadhil Ahamed Reddy, Tiyyagura Mohit Subba Reddy, Sabbella Manoj Vikas, Ambati Sai Mathi, Senthilkumar Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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Developing an Instrument for Cloud-Based E-Learning Adoption: Higher Education Institutions Perspective

Developing an Instrument for Cloud-Based E-Learning Adoption...

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International Conference on Computer, Communication and Computational Sciences, IC4S 2019

作者： AlAjmi, Qasim Arshah, Ruzaini Abdullah Kamaludin, Adzhar Al-Sharafi, Mohammed A. College of Arts & Humanities A’ Sharqiyah University Ibra Oman Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak GambangPahang26300 Malaysia Faculty of Educational Technology A‘Sharqia University Ibra Oman

ISBN: (纸本)9789811544088

Cloud-Based E-learning is indeed the new paradigm shift in the information and communication technology. As much as the field is still in its infancy stage, various educational institutions have adopted the cloud-based E-learning around the world at all the levels of education. Cloud-based E-learning has shown significant progress and success among all the fields and subjects of education including language teaching, mathematics, science, engineering, and technology. Concisely, this paper is to show the instrument development that can evaluate the adoption of the cloud-based E-learning based on two vital learning aspects: the approach of systematic review and the interaction among the concerned and experts’ point of views. The practitioners in the field of education may find this instrument appealing because of its effectiveness of use. The purpose of this article is to develop multi-dimensional instrument that can be applied to measure the adoption factors for cloud-based E-learning perceived by HEIs, and to come up with a set of validated items. A systematic research design was implemented, multiple stage followed, six experts evaluated this instrument, piloting on 40 out of 100 respondents. Data collection triangulated by in-person interview, telephone interview, and questionnaire interview. Finally, empirically validation was done along with response bias. Finally, valid instrument is developed. © 2021, Springer Nature Singapore Pte Ltd.

关键词： E-learning

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Fuzzy linear model for production optimization of mining systems with multiple entities

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International Journal of Minerals,Metallurgy and Materials 2011年第6期18卷 633-637页

作者： Slobodan Vujic Tomo Benovic Igor Miljanovic Marjan Hudej Aleksandar Milutinovic Petar Pavlovic Faculty of Mining and Geology Department of Applied Computing and System Engineering University of Belgrade Mine and Thermal Power Plant Ugljevik Ugljevik 76330 Bosnia and Herzegovina Coal Mine Velenje Velenje 3320 Republic of Slovenia High School of Technology Professional Studies Sabac 15000 Serbia

Planning and production optimization within multiple mines or several work sites （entities） mining systems by using fuzzy linear programming （LP） was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.

关键词： linear programming fuzzy set theory optimization production planning bauxite mines

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Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches

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Scientific Reports 2025年第1期15卷 1-19页

作者： Imra Aqeel Abdul Majid Abdullah Albanyan Hassan Wasfi Biomedical Information Research Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad 45650 Pakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Rabigh 21911 Saudi Arabia

The COVID-19 pandemic has initiated a global health emergency, with an exigent need for an effective cure. Progressively, drug repurposing is emerging as a promising solution for saving time, cost, and labor. However, the number of drug candidates that have been identified for the treatment of COVID-19 is still insufficient, so more effective and thorough drug exploration strategies are required. In this study, we joined the molecular docking with machine learning approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the replicating enzyme3CLproof SARS-CoV-2. Molecular docking is used to calculate the binding affinities of these drugs towards3CLpro. We employed several machine learning approaches for QSAR modeling to explore some potential drugs with high binding affinities. Our outcomes demonstrated that the Decision Tree Regression (DTR) model, with the best scores of R² and RMSE, is the most suitable model to explore the potential drugs. We shortlisted six favorable drugs with their respective Zinc IDs(3873365,85432544,203757351,85536956,8214470,and 261494640)within the range of -15 kcal/mol to -13 kcal/mol. We further examined the physiochemical and pharmacokinetic properties of these most potent drugs. Our study provides an efficient framework to explore the potential drugs against COVID-19 and establishes the impending combination of molecular docking with machine learning approaches to accelerate the identification of potential therapeutic candidates. Our verdicts contribute to the larger goal of finding effective cures for COVID-19, which is an acute global health challenge. The outcomes of our study provide valuable insights into potential therapeutic candidates for COVID-19 treatment.

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